Secure distributed Kalman filter using partially homomorphic encryption

Sadeghikhorami, Ladan; Safavi, Ali Akbar

doi:10.1016/j.jfranklin.2020.08.048

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…A secure state estimation using Kalman filter with the adoption of a hybrid homomorphic encryption scheme was proposed in [23]. Authors in [24] presented a multi-party dynamic state estimation using the Kalman filter and PHE, while [25] owners introduced a secure distributed Kalman filter using PHE. However, no work to date has provided a computational privacy investigation for estimation algorithms that use Kalman filters along with PHE, considering that other problems have undergone similar computational privacy analysis, such as set-based estimation in [5] and quadratic optimization in [21].…”

Section: Introductionmentioning

confidence: 99%

“…where xq,0 , P q,0 is the initial estimates from the query node and xa,k , P a,k is the k estimates on the aggregator side. The view of the coalition V Π sa is constructed from ( 23)- (25). The simulator of the coalition can be denoted by S sa = S K sa , where S K sa is the simulator after executing K iterations.…”

mentioning

confidence: 99%

“…where I k s is given in (25), and I k q are the new data added from the k-th iteration for the query node with V Π,0 q = I 0 q such that I k q = xq,0 , P q,0 , xa,k , P a,k , xa,k , pk, sk, coins q , Γ sq .…”

mentioning

confidence: 99%

See 2 more Smart Citations

Privacy Guarantees for Cloud-based State Estimation using Partially Homomorphic Encryption

Alanwar¹,

Alkabani²,

El-Kharashi³

et al. 2021

Preprint

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The privacy aspect of state estimation algorithms has been drawing high research attention due to the necessity for a trustworthy private environment in cyber-physical systems. These systems usually engage cloud-computing platforms to aggregate essential information from spatially distributed nodes and produce desired estimates. The exchange of sensitive data among semi-honest parties raises privacy concerns, especially when there are coalitions between parties. We propose two privacy-preserving protocols using Kalman filter and partially homomorphic encryption of the measurements and estimates while exposing the covariances and other model parameters. We prove that the proposed protocols achieve satisfying computational privacy guarantees against various coalitions based on formal cryptographic definitions of indistinguishability. We evaluated the proposed protocols to demonstrate their efficiency using data from a real testbed.

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